Methods and systems for determining a correlation between patient actions and symptoms of a disease

ABSTRACT

Example embodiments disclosed herein relate to methods and systems for determining whether particular actions affect or influence medical symptoms of patients. In one example, a plurality of datasets from a corresponding plurality of patients is received, where each patient has a corresponding disease. An individual dataset for an individual patient may include information about at least one disease symptom of the patient and at least one action of the patient. After the datasets are received from the patients, the datasets are stored in a database. Using the datasets stored in the database, a correlation between one or more actions and one or more disease symptoms may be determined based on a statistical analysis of the actions and symptoms stored in the database. The correlation between the one or more actions and the one or more disease symptoms may also be stored in the database.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a U.S. National Phase of PCT applicationPCT/US14/13894, filed on Jan. 30, 2014 and currently pending. ThePCT/US14/13894 application claims priority to provisional applicationnumbers 61/759,231 filed on Jan. 31, 2013, 61/762,033 filed on Feb. 7,2013, and 61/860,893 filed on Jul. 31, 2013. The entire contents of thePCT/US14/13894, 61/759,231, 61/762,033, and 61/860,893 applications areincorporated herein by reference.

FIELD

The present disclosure relates generally to methods and systems fordetermining whether particular actions (eating certain foods, takingspecific medications, or engaging in particular exercises or otheractivities, for example) affect or influence medical symptoms ofpatients. More specifically, the present disclosure relates to methodsand systems for determining a correlation between one or more actions ofpatients and one or more symptoms of a disease based on a statisticalanalysis of the actions and symptoms.

BACKGROUND

Predictive models have been, and are, commonly used to predict medicaloutcomes. Such models are based on statistical data obtained frompopulations of individuals that are identified as having or not having aparticular medical outcome. The data regarding the populations ofindividuals is typically analyzed using statistical techniques such aslinear regression or multiple linear regression (MLR), to predict themedical outcome. Generally, the models involve using controllable and/oreasily measured variables (i.e., factors) to explain or predict thebehavior of other variables (responses).

In its simplest form, a MLR specifies the linear relationship between adependent variable (response) Y, and a set of independent predictorvariables (factors) Xs, so that every value of the independent variableX is associated with a value of the dependent variable Y. For many dataanalysis problems, estimates of the linear relationships between thesevariables are adequate to describe the observed data, and to makereasonable predictions for new observations. For example, the weight ofa person could be estimated as a function of the height of the personand the amount of exercise the person engages in. Using MLR, respectiveregression coefficients may be determined from a sample of data,measuring height and observing the amount of exercise of the subjects.

One problem with using the foregoing conventional predictive models topredict medical outcomes is that the models rely on factors that are fewin number, are not significantly redundant (collinear), and have a wellunderstood relationship to the responses. However, if any of theseconditions vary (i.e., break down), these MLRs can be inefficient orinappropriate. Stated differently, if the number of factors gets toolarge, then the model will likely be unable to predict new data. Thismay be due to the fact that, though there are many factors, there may beonly a few latent variables that account for most of the variation inresponse. Accordingly, using MLR to predict medical outcomes based on alarge number of factors may not be feasible in some circumstances.

SUMMARY

This application discloses methods and systems for determiningcorrelations between one or more actions of patients and one or moredisease symptoms based on statistical analysis of datasets of patientactions and disease symptoms built from inputs received from a pluralityof patients over time. In some embodiments, the method may includereceiving and analyzing a single dataset pertaining to one patient. Someembodiments further consider patient characteristics and whethercorrelations of patient actions and disease symptoms vary according toparticular patient characteristics. In this manner, the disclosedsystems and methods are able to predict correlations between patientactions and disease symptoms based on data collected from a sufficientplurality of individual patients (or a sufficient amount of datapertaining to a single patient), and in turn, predict for an individualpatient, whether performing a particular action is likely to improve adisease symptom for that particular patient.

Some embodiments include a method comprising receiving, at a computingdevice, a dataset from a patient. The dataset includes information aboutat least one disease symptom of the patient and at least one action ofthe patient. The method includes storing the dataset in a databasecomprising a tangible, non-transitory computer readable media. Themethod also includes determining a correlation between one or moreactions and one or more symptoms of a disease based on a statisticalanalysis of the actions and symptoms described in the dataset. Themethod additionally includes storing in the database the correlationbetween the one or more actions and the one or more symptoms.

Some embodiments include receiving and storing a plurality of datasetsfrom a corresponding plurality of patients, wherein the dataset for anindividual patient includes information about at least one diseasesymptom and at least one action of the individual patient. Suchembodiments further include determining and storing a correlationbetween one or more actions and one or more symptoms of the diseasebased on a statistical analysis of the actions and symptoms of theplurality of datasets received from the corresponding plurality ofpatients.

Some embodiments include a system comprising one or more processorsconfigured to receive a dataset from a patient. The dataset includesinformation about at least one disease symptom of the patient and atleast one action of the patient. The one or more processors are furtherconfigured to (1) store the dataset in a database comprising a tangible,non-transitory computer readable media; (2) determine a correlationbetween one or more actions and one or more symptoms of a disease basedon a statistical analysis of the actions and symptoms of the of dataset;and (3) store in the database the correlation between the one or moreactions and the one or more symptoms.

Some embodiments include a computing system configured to receive andstore a plurality of datasets from a corresponding plurality ofpatients, wherein the dataset for an individual patient includesinformation about at least one disease symptom and at least one actionof the individual patient. In such embodiments, the computing system isfurther configured to determine and store a correlation between one ormore actions and one or more symptoms of the disease based on astatistical analysis of the actions and symptoms of the plurality ofdatasets received from the corresponding plurality of patients.

Still further embodiments include a non-transitory computer readablemedium having stored therein instructions executable by a computersystem to cause the computer system to perform certain functions. Thefunctions include receiving a dataset from a patient. The datasetincludes information about at least one disease symptom of the patientand at least one action of the patient. The functions include storingthe dataset in a database comprising a tangible, non-transitory computerreadable media. The functions also include determining a correlationbetween one or more actions and one or more symptoms of a disease basedon a statistical analysis of the actions and symptoms of the dataset,and storing in the database the correlation between the one or moreactions and the one or more symptoms.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the figures and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the subject matter described herein will now beexplained with reference to the accompanying drawings of which:

FIG. 1 illustrates a block diagram of an example system according tocertain embodiments;

FIG. 2A illustrates a simplified embodiment of an example web serverthat may be used with the system of FIG. 1;

FIG. 2B illustrates a simplified embodiment of an example client devicethat may be used with the system of FIG. 1;

FIG. 3A illustrates a flow chart of one example embodiment fordetermining a correlation between one or more actions of patients andone or more symptoms of a disease of the patient based on a statisticalanalysis of the actions and symptoms;

FIG. 3B illustrates a flow chart according to one alternative embodimentfor determining a correlation between one or more actions of patientsand one or more symptoms of a disease of the patient based on astatistical analysis of the actions and symptoms;

FIG. 3C illustrates a flow chart according to another alternativeembodiment of a method for determining a correlation between one or moreactions of patients and one or more symptoms of a disease of the patientbased on a statistical analysis of the actions and symptoms; and

FIG. 4 illustrates a flow chart for an algorithm 400 according to someembodiments for determining a correlation and testing the correlationwith a patient population.

FIG. 5 illustrates an example computer program product according to oneembodiment.

DETAILED DESCRIPTION

The following detailed description describes various features andfunctions of the disclosed systems and methods with reference to theaccompanying figures. In the figures, similar symbols typically identifysimilar components, unless context dictates otherwise. The exampleembodiments described herein are not meant to be limiting. Otherembodiments may be utilized, and other changes may be made, withoutdeparting from the scope of the subject matter presented herein. It willbe readily understood that the aspects of the present disclosure, asgenerally described herein and illustrated in the figures can bearranged, substituted, combined, separated, and designed in a widevariety of different configurations, all of which are contemplatedherein.

1. Medical Predictive Model Overview

In general, multiple linear regression (MLR) can be used to createpredictive models for medical outcomes. Partial least squares regression(PLSR) extends MLRs without imposing known restrictions employed bymultivariate MLR. This flexibility allows PLSR to be used in situationswhere the use of traditional multivariate methods may be severelylimited, such as when there are fewer observations than predictorvariables. Furthermore, PLSR can be used as an exploratory analysis toolto select suitable predictor variables and to identify outliers beforeapplying classical MLR.

One feature of the methods and systems described herein is the abilityto generate a predictive model for medical outcomes in the instanceswhere the medical outcomes may be based on a plurality of variables.Another feature of the methods and systems disclosed herein is to usethe generated predictive model to make recommendations to patients. Morespecifically, the methods and systems described herein determine acorrelation between one or more actions of patients and one or moresymptoms of a disease of the patients based on a statistical analysis ofthe actions and symptoms. Using this statistical information,recommendations may be made to the patients to undergo or take (or avoidundergoing or taking) specific actions. In certain embodiments,symptoms, actions and correlations are related interactively, whereinthe effect of at least one patient's actions are evaluated with regardto the effects on patient symptoms and on the correlation betweenpatient actions and symptoms, and wherein actions are recommended toother patients to undergo or take (or avoid undergoing or taking) thathave a predicted effect on that patient's disease or symptoms.

Example embodiments disclosed herein relate to methods and systems thathelp determine whether particular actions (eating or avoiding certainfoods, taking or avoiding specific medications, or engaging in oravoiding particular exercises or other activities, for example) affector influence medical symptoms of patients. In one example, a pluralityof datasets from a corresponding plurality of patients is received at acomputing device. (In some embodiments, the method may include receivinga single dataset pertaining to one patient.) Each patient may have atleast one corresponding disease. An individual dataset of each patientmay include information about at least one disease symptom of thepatient and at least one action of the patient. After the datasets arereceived from each patient, the datasets may be stored in a database.Using the datasets stored in the database, a correlation between one ormore actions and one or more symptoms of the disease may be determinedbased on a statistical analysis of the actions and symptoms stored inthe database. The correlation between the particular action and the atleast one symptom may also be stored in the database.

2. Network Environment

FIG. 1 illustrates a block diagram of one example system in which themethods disclosed herein may be implemented. The illustrated systemincludes a web server 102, client devices 108 a, 108 b, and 108 c . . ., 108(n) each configured to communicate with web server 102 and datastorage 110 directly or indirectly over the Internet 106.

Web server 102 may be any computing device capable of carrying out themethods and functions described herein. Web server 102 may include oneor more web-based applications 104 a-c that may be configured to performthe methods disclosed herein. For example, the web-based applications104 a-c may be configured to perform one or more steps of the methodsdiscussed in reference to FIGS. 3A-3C and 4. The web applications may beimplemented using numerous web application frameworks including, forexample, PHP, JavaServer, ASP.NET, Cold Fusion, or similar webapplications. However, the preceding examples are included forillustrative purposes only, as many other web application frameworksexist and may be used to implement the web applications. Web server 102may also include other components that are discussed in more detaillater in this disclosure, such as a processor, one or more memorycomponents, or one or more network interfaces, for example.

The client devices 108 a, 108 b, and 108 c may be used to help carry outthe methods described herein. The client devices may be any sort ofcomputing device, such as a laptop computer, desktop computer, networkterminal, mobile computing device (e.g., smart phone), etc. In otherillustrative embodiments, the client devices may take the form of aportable media device, personal digital assistant, notebook computer, orany other mobile device capable of accessing web server 102 over theInternet 106.

Data storage 110 may include one or more computer-readable storage mediathat can be read or accessed by at least one of the client devices 108a, 108 b, and 108 c or web server 102, for example. The one or morecomputer-readable storage media may include volatile and/or non-volatilestorage components, such as optical, magnetic, organic or other memoryor disc storage. In some embodiments, the data storage 110 may beimplemented using a single physical device (e.g., one optical, magnetic,organic or other memory or disc storage unit), while in otherembodiments, the data storage 110 may be implemented using two or morephysical devices. In FIG. 1, data storage 110 is depicted as a“cloud-based” data storage unit (i.e. storage that is off-site withrespect to web server 102 or client devices 108 a-c). However, datastorage 110 may also be implemented on-site with respect to web server102 or client devices 108 a-c.

Data storage 110 may contain data (e.g., patient data sets, correlationdata, etc.) capable of being manipulated by a processor, as well asstored program logic executable by the processor. By way of non-limitingexample, data in data storage 110 may contain one or more storeddatasets that include patient information, information about diseasesymptoms of the patients, and actions of the patients. In some examples,data storage 110 may also contain data representing recommended actionsto be taken by patients and information on correlations determined andtested by the system. Additionally, data storage 110 may also containstored program logic that is executable by a processor of web server 102(e.g., shown in FIG. 2) to carry out the various software functionsdescribed herein. Data storage 110 may be provided via a SAAS (softwareas a service) or PAAS (platform as a service) implementation. Someembodiments may use Catalyse API for data storage. Instructions forperforming methods disclosed herein may also be stored and accessed viaSAAS and/or PAAS implementations. The SAAS and PAAS data storageexamples are included for illustrative purposes only, as many otherstorage arrangements could be implemented without departing from thescope of disclosed systems and methods.

3. Web Server Architecture

FIG. 2A is a simplified block diagram depicting an example web server200 configured to operate in accordance with various embodiments. Webserver 200 may be similar or identical to web server 102 discussed inreference to FIG. 1. Web Server 200 may be a personal computer, laptopcomputer, or some other type of device that communicates with othercommunication devices via point-to-point links or via a network, such asInternet 106 shown in FIG. 1. In a basic configuration, web server 200may include one or more processors 202, data storage 204, and acommunication interface 206. A memory bus 208 can be used forcommunicating among the processor 202, data storage 204, and thecommunication interface 206.

Processor 202 may include one or more CPUs, such as one or more generalpurpose processors and/or one or more dedicated processors (e.g.,application specific integrated circuits (ASICs) or digital signalprocessors (DSPs), etc.). Data storage 204, in turn, may comprisevolatile and/or non-volatile memory and can be integrated in whole or inpart with processor 202. Data storage 204 may hold program instructionsexecutable by processor 202, and data that is manipulated by theseinstructions, to carry out various functions described herein.Alternatively, the functions can be defined by hardware, firmware,and/or any combination of hardware, firmware, and software.

Communication interface 206 may take the form of a wired or wirelessconnection, perhaps operating according to IEEE 802.11 or any otherprotocol or protocols used to communicate with other communicationdevices or a network. Other forms of physical layer connections andother types of standard or proprietary communication protocols may beused over communication interface 206. Furthermore, communicationinterface 206 may comprise multiple physical or logical networkinterfaces, each capable of operating according to the same or differentprotocols.

In addition to the components and functions discussed above, web server200 may support additional components and functions, includingcomponents and functions used to perform any of the methods describedherein.

4. Client Device Architecture

FIG. 2B is a simplified block diagram depicting an example client device220 configured to operate in accordance with various embodiments. Clientdevice 220 may be similar or identical to client devices 108 a-c,discussed in reference to FIG. 1.

The client device 220 may include a user interface module 222, acommunication interface 226, one or more processors 224, and datastorage 228, all of which may be linked together via a system bus,network, or other connection mechanism 230. The user interface module222 may be operable to send data to and/or receive data from externaluser input/output devices. For example, the user interface module 222may be configured to send/receive data to/from user input devices suchas a keyboard, a keypad, a touch screen, a computer mouse, a track ball,a joystick, and/or other similar devices, now known or later developed.The user interface module 222 may also be configured to provide outputto user display devices, such as one or more cathode ray tubes (CRT),liquid crystal displays (LCD), light emitting diodes (LEDs), LEDdisplays, displays using digital light processing (DLP) technology,printers, light bulbs, and/or other similar devices, now known or laterdeveloped. The user interface module 222 may also be configured togenerate audible output(s), such as via a speaker, speaker jack, audiooutput port, audio output device, earphones, and/or other similardevices, now known or later developed. The communications interfacemodule 226 may be configurable to communicate via a network, such as theInternet 106 shown in FIG. 1.

Communication interface 226 may take the form of a wired or wirelessconnection, perhaps operating according to IEEE 802.11, CDMA, 3G, LTE,4G, or any other protocol or protocols used to communicate with othercommunication devices or a network. Other forms of physical layerconnections and other types of standard or proprietary communicationprotocols may be used over communication interface 226. Furthermore,communication interface 226 may comprise multiple physical or logicalnetwork interfaces, each capable of operating according to the same ordifferent protocols.

The data storage 228 may include computer-readable program instructionsand perhaps additional data. In some embodiments, the storage 228 mayadditionally include storage required to perform at least part of theherein-described techniques and/or at least part of the functionality ofthe herein-described devices and systems. For example, data storage 228may store data corresponding to patient actions, characteristics, and/orsymptoms, and it may also store instructions executable to performstatistical analysis of the data or other methods disclosed herein. Insuch a case, the statistical analysis may be performed by the clientdevice 220.

The one or more processors 224 may include one or more general purposeprocessors (e.g., microprocessors manufactured by Intel or AdvancedMicro Devices) and/or one or more special purpose processors (e.g.,digital signal processors, application specific integrated circuits,etc.). The one or more processors 224 may be configured to executecomputer-readable program instructions that are contained in the datastorage 228 and/or other instructions as described herein. Instructionscontained in data storage 228 may be executable by processor 224 so thatclient device 220 may execute at least part of the methods disclosedherein. The data storage 228 may include one or more computer-readablestorage media that can be read or accessed by at least one of theprocessors 224. The one or more computer-readable storage media mayinclude volatile and/or non-volatile storage components, such asoptical, magnetic, organic or other memory or disc storage, which can beintegrated in whole or in part with at least one of the processors 224.In some embodiments, the data storage 228 may be implemented using asingle physical device (e.g., one optical, magnetic, organic or othermemory or disc storage unit), while in other embodiments, the datastorage 228 may be implemented using two or more physical devices.

5. Determining Correlations Between Patient Actions and Disease Symptoms

FIG. 3A illustrates a method 300 for creating a predictive model of amedical outcome, according to example embodiments. This method may becarried out, for example, using the network environment 100 described inreference to FIG. 1. Method 300 may include one or more operations,functions, and/or actions as illustrated by one or more of blocks301-304. Although the blocks are illustrated in a sequential order,these blocks may also be performed in parallel, and/or in a differentorder than those described herein. Also, the various blocks may becombined into fewer blocks, divided into additional blocks, and/orremoved based upon the desired implementation.

In addition, for the method 300 and other processes and methodsdisclosed herein, the flowchart shows functionality and operation of onepossible implementation of present embodiments. In this regard, eachblock may represent a module, a segment, or a portion of program code,which includes one or more instructions executable by a processor orcomputing device for implementing specific logical functions or steps inthe process. The program code may be stored on any type of computerreadable medium or memory, for example, such as a storage deviceincluding a disk or hard drive or other form of memory media. Thecomputer readable medium may include non-transitory computer readablemedium, for example, such as computer-readable media that stores datafor short periods of time like register memory, processor cache andRandom Access Memory (RAM). The computer readable medium may alsoinclude non-transitory media, such as secondary or persistent long termstorage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable media may also be any other volatile or non-volatile storagesystems. The computer readable medium may be considered a computerreadable storage medium, for example, or a tangible storage device.

First, at step 301, the method includes receiving, at a computingdevice, a plurality of datasets from a corresponding plurality ofpatients. In some embodiments, the method may include receiving a singledataset pertaining to one patient or a plurality of datasets pertainingto that one patient. Each patient has at least one correspondingdisease, and an individual dataset for an individual patient may includeinformation about at least one disease symptom of the individual patientand at least one action of the individual patient. The computing devicemay be, for example, any computing device capable of acting as webserver 102 and running web applications 104 a-c. The datasets may bereceived from a user (e.g., input by a user or reported by monitoringdevices associated with the user (e.g., heart rate monitors, bloodglucose monitors, body temperature monitors, personal activity monitors,etc.)), for example. In some instances the user may be the patient, andin others the user may be a person acting on behalf of the patient. Inone example, multiple users (e.g., patients) may use the client devices108 a, 108 b, and/or 108 c to enter in their corresponding dataset. Eachuser may, for example, utilize a graphical user interface displayed viaa web page or a native application on the client device 108 a-c to entertheir respective dataset. In other examples, the dataset may be receivedfrom a server or other network, for example.

The disease of each patient may be any currently known disease, and thedisease symptom may be any symptom that corresponds to the disease. Insome circumstances the symptom may be a new and previously unknownsymptom. The action may be any action that may influence or affect themedical condition of the patient and could be a new and previouslyunknown action. For example, the action may include consumption of aparticular food product, ingestion of a particular therapeutic agent,application of a particular therapeutic agent, ingestion of a particulardietary supplement, performance of a particular physical activity, orexposure to a particular chemical agent. Alternatively, the action mayinclude avoiding or refraining from consuming a particular food product,avoiding or refraining from ingesting a particular therapeutic agent,avoiding or refraining from using a particular therapeutic agent,avoiding or refraining from ingesting a particular dietary supplement,avoiding or refraining from performing a particular physical activity,or avoiding exposure to a particular chemical agent. The action may alsoinclude exposure to or avoiding exposure to an environmental factor,such as such as certain weather or temperature conditions like rain,snow, high or low humidity, or high or low temperatures. In someexamples, an action may include multiple actions or a quantification ofan aspect of an action. For instance, an action may include applying atherapeutic agent according to a particular schedule for a period oftime, e.g., three times a day for ten days. Other actions are possibleand contemplated herein.

After the datasets are received, the process then proceeds to step 302.At step 302, the received datasets are stored in a database comprising atangible, non-transitory computer readable media. In some embodiments,the method may include storing a single dataset or a plurality ofdatasets pertaining to one patient. The computer readable media mayinclude non-transitory computer readable media, for example, such ascomputer-readable media that stores data for short periods of time likeregister memory, processor cache and Random Access Memory (RAM). Thecomputer readable media may also include non-transitory media, such assecondary or persistent long term storage, like read only memory (ROM),optical or magnetic disks, compact-disc read only memory (CD-ROM), forexample. The computer readable media may also be any other volatile ornon-volatile storage systems. In another example, the computerreadable-media may take the form of the computer-readable medium asdescribed with reference to FIG. 5.

In preferred embodiments, the computing device receives datasets from aplurality of patients over varying timeframes, e.g., days, weeks,months, or even years. Preferably, the plurality of patients includes astatistically significant number of patients from which to determinecorrelations between patient actions and disease symptoms (and in somecases, correlations between patient actions, disease symptoms, andpatient characteristics). But, in some embodiments, the correlation maybe generated for a single patient based on a dataset including only thatpatient's past actions, disease symptoms and/or characteristics.However, preferably, the plurality of patients is quite large, such ason the order of a few thousand to tens of thousands. However, a smallerplurality of patients could be used with a corresponding reduction inconfidence levels corresponding to determined correlations. Inoperation, an individual dataset corresponding to an individual patientmay contain inputs received from that patient over a span of days,weeks, months, or even years. Thus, over time, the database will containmany similar datasets from many different patients having the samedisease. Indeed, the database can store datasets from many differentpatients, e.g., many hundreds, thousands, or even millions of patients.

The database may be a relational database or any other type of database,and may be implemented with data storage that is the same or similar tothe data storage 110 discussed with reference to FIG. 1. The databasemay include other information about each of the patients. For example,the database may include patient characteristics comprising a medicalprofile for the patient including, for example, the age, sex, height,weight, allergies, ethnicity, medications, medical history, or othercharacteristics of the patient. The database may also store more or lessinformation regarding the current disease of each patient. For example,the database may include information regarding the severity of thedisease or a detailed description of the disease. Other information thatis relevant to the medical background of the patient may be included aswell.

Once the data has been stored, at step 303, a correlation between one ormore actions and one or more disease symptoms may be determined based ona statistical analysis of the actions and symptoms of the plurality ofdatasets received from the corresponding plurality of patients. In someembodiments, the method may include determining a correlation betweenone or more actions and one or more symptoms included in a singledataset that pertains to a single patient. To do so, various algorithmsand processes may be used. In one embodiment, determining thecorrelation may include performing a multivariate analysis of, forexample, the actions and symptoms stored in the database. Themultivariate analysis may be performed using Equations 1 and 2, forexample.

5.1 Equation 1X=TP ^(T) with TT ^(T) =I

5.2 Equation 2Y=TBC ^(T)

Equations 1 and 2 allow a fundamental relationship between two matrices,X and Y, to be determined. In Equations 1 and 2, I observationsdescribed by K dependent variables are stored in an I×K matrix denotedY, and the values of J predictors collected on these I observations arecollected in the I×J matrix X. In Equations 1 and 2, I represents theidentity matrix; T represents the score matrix; and P represents theloading matrix. In Equation 2, B represents a diagonal matrix withregression weights as diagonal elements. Using these equations, Y can bepredicted from matrix X.

5.3 Equation 3Y=XB+E

Equation 3 is a simplified variant of Equations 1 and 2. Similar toEquations 1 and 2, Equation 3 also allows a fundamental relationshipbetween two matrices, X and Y, to be determined. In Equation 3, similarto Equations 1 and 2, the I observations described by K dependentvariables are stored in an I×K matrix denoted Y, and the values of Jpredictors collected on these I observations are collected in the I×Jmatrix X. B represents a P by M regression coefficient matrix, and E isa noise term for the model which has the same dimensions as Y. Usingthis equation, Y can be predicted from matrix X. The final prediction ofY should be the same for this variation of PLSR, but the componentsdiffer from those set out in Equations 1 and 2.

Within the context of this disclosure, the observations are meant to bethe patients, and the predictors are the actions (and also thecharacteristics in some instances) of the patients. The predictors maybe any of the actions discussed with reference to step 301, for example.The responses, or value of the predictors, are the medical outcomes orsymptoms. Accordingly, I=patients, J=actions; and K=medical outcomes orsymptoms. In some embodiments, the prediction functionality may beimplemented with proprietary or open-source machine learning systems orSaaS services such as BigML.

For example, a researcher may want to predict the subjective evaluationof a headache. The dependent variables that the researcher may like topredict for the headache are the duration and severity of the headache(i.e., disease symptoms). The predictors may be the amount of medicinetaken and amount of sleep obtained (i.e., patient actions). UsingEquations 1 and 2, or Equation 3, a subset of latent variables or theirlinear or non-linear combination may be extracted that may explain theduration and/or severity of the headache.

In another example embodiment, determining the correlation between oneor more actions and one or more symptoms of a disease based on astatistical analysis of the actions and symptoms of the plurality ofdatasets received from the corresponding plurality of patients mayinclude modeling a Boolean network of the actions and symptomspreviously stored in the database at step 302, for example. In someembodiments, the method may include modeling a Boolean network of theactions and symptoms previously stored in the database from a singledataset or datasets that pertains to a single patient. A Boolean networkmay include a number of discrete binary variables—such as patientactions, characteristics, or symptoms—that are interrelated via Booleanfunctions that define dependencies of some variables upon others. Inother embodiments, neural networks or other machine learning techniquesor predictive algorithms may be used to determine a correlation betweenpatient actions and disease symptoms.

After the correlation has been determined, at step 304, the correlationbetween the particular action and the at least one symptom may be storedin the database. The correlation may be stored in a manner similar tothe datasets discussed above with reference to step 302, for example. Inone instance, the correlation is stored in a manner that associates thecorrelation with the relevant patients.

In preferred embodiments, the correlation is based on a plurality ofpatient datasets collected from patient data received from a pluralityof patients over time. Thus, the correlation determined at step 304 maybe based upon an analysis of data aggregated from many hundreds,thousands, or even millions of patient datasets. However, in otherembodiments the correlation may be based on a dataset or datasetscollected from a limited number of patients (even a single patient insome instances.)

Additionally, correlations between specific patient actions and diseasesymptoms may be stored in the database. In operation, each correlationmay have a confidence factor or similar assessment corresponding to thestrength of the correlation between the action and its effect on adisease symptom. For example, if the datasets showed that eating threeservings of broccoli a week lowered blood pressure by 30% in 85% ofpatients with heart disease, then consumption of three servings ofbroccoli per week would be considered highly correlated with loweringblood pressure. Likewise, if consuming three servings of peanut butter aweek lowered blood pressure by 5% in 15% of patients with heart disease,then consumption of that amount of peanut butter would be consideredweakly correlated with lowering blood pressure. But if consuming sevenservings of peanut butter a week (i.e., once a day) lowered bloodpressure by 30% in 50% of patients, then daily consumption of peanutbutter would be considered highly correlated with lowering bloodpressure. In operation, correlations between actions and diseasesymptoms can be reassessed as more data is received from patients. Inthis manner, each determined correlation and the corresponding strengthor weakness of correlation can be continually (or least frequently)revised as new data is received from patients.

FIG. 3B shows another example embodiment of additional or alternativesteps of the method 300 shown in FIG. 3A. In FIG. 3B, the methodadditionally includes steps 305-308. At step 305, the computing devicemay receive a query regarding whether a correlation exists between aparticular action and a particular disease symptom.

In response to receiving the query, at step 305, the computing devicemay query the database to determine whether the database includes acorrelation between the particular action and the particular symptom forthe particular disease. The particular action and particular symptom maybe any of the symptoms and actions discussed above with regard to steps300-304.

In response to determining that the database includes a correlationbetween the particular action and the particular symptom, the computingdevice may send an indication of the correlation, and in response todetermining that the database does not include a correlation between theparticular action and the particular symptom, the computing device maysend an indication that the database does not include a correlationbetween the particular action and the particular symptom. Sending anindication may comprise sending a notification to one of a client device108 a-c of which a patient is using, for example. The notification maycomprise any signal or message capable of relaying the information, forexample.

In some embodiments, the indication sent by the computing device maycorrespond to at least one of the following types: (i) sufficient datahas been collected and analyzed to conclude that performing theparticular action (or refraining from the action) has been correlatedwith an improvement (or worsening) of the particular disease symptom;(ii) sufficient data has been collected and analyzed to conclude thatperforming the particular action (or refraining from the action) has notbeen found to be correlated with an improvement (or worsening) of theparticular disease symptom; or (iii) sufficient data has not beencollected and analyzed to conclude that performing the particular action(or refraining from the action) has any correlation to an improvement(or worsening) of the particular disease symptom (i.e., insufficientdata). As the system collects more data from the plurality of patientsover time, the ability of the system to determine correlations and sendresponsive indications of the first two of the three types shouldimprove.

FIG. 3C shows an even further example embodiment of additional oralternative steps of the method shown in FIG. 3A. In FIG. 3C, the methodadditionally includes steps 309-312. At step 309, the computing devicemay further be configured to send instructions to at least one patientto perform a specific action. The specific action may be selected totest a particular correlation stored in the database. For example, if aparticular correlation is suspected between the particular action andthe particular symptom—from an indication returned from steps 305-308 ofFIG. 3B, for example—then the computing device may suggest modificationsto the particular habits or activities of the particular patient. As thepatient engages in the new activity, new data may be obtained from thepatient based on that activity, and at step 310, the computing devicemay be configured to receive from the patient, inputs associated withthe performance of the specific actions of the patient and at least onesymptom of the disease of the patient. The new data may be obtained fromthe patient in a manner similar to that explained in reference to step301, for example.

Once the inputs associated with the performance of the specific actionof the patient and at least one symptom of the disease of the patienthave been received, the particular correlation based on the inputsreceived from the at least one patient may be updated, and any updatesmay be stored in the database. In some embodiments, a new correlationmay be made based on the inputs received from the patient. In such aninstance, the correlation may be determined, for example, using amultivariate statistical analysis. The multivariate analysis may beperformed using the method described with reference to FIG. 3A, forexample. In some embodiments, the inputs collected from the patientafter the patient has performed certain actions and the correspondingeffects on the patient's disease symptoms (if any) may be statisticallyanalyzed separately from other action and symptom inputs collected fromthe patient. In such embodiments, conducting the statistical analysis ofthese particular actions and effects separately may improve thelikelihood of identifying a correlation between a particular action andany change (improvement or degradation) in disease symptoms.

In operation, if one patient performs an action that improves one ofthat patient's disease symptoms, then the system may advise otherpatients to perform that same action to determine whether and the extentto which that action improves the same disease symptom in the otherpatients, thereby collecting data to verify or disprove a potentialcorrelation. For example, in some embodiments, once the systemrecognizes a potential correlation between a particular action and acertain disease symptom, the system may instruct additional patients toperform that particular action for the purpose of collecting furtherdata from the additional patients who perform the particular action inresponse to the instruction.

If the further data collected from the additional patients does notestablish a sufficient statistical correlation between the particularaction and an improvement in the disease symptom, then the system mayconclude that the particular action and the disease condition are notstatistically correlated, thus disproving the potential correlation. Butif the further data collected from the additional patients corroboratesthe potential correlation, then the system may instruct even morepatients to perform that particular action to obtain sufficient data todetermine that a correlation exists between the particular action andthe disease symptom. Thus, in this manner, the system tests potentialcorrelations across different patient populations over varyingtimeframes to either verify or disprove newly-identified potentialcorrelations. And once the system has sufficient statistical informationto verify that a potential correlation is an actual correlation, thesystem may instruct still further patients to perform the particularaction and to collect data from the still further patients to improvethe reliability (or confidence factor) associated with the verifiedcorrelation.

FIG. 4 illustrates a flow chart for an algorithm 400 according to someembodiments for (i) determining a correlation between one or morepatient actions, a disease symptom, and one or more patientcharacteristics, and (ii) testing the determined correlations.

Algorithm 400 starts at block 401, where datasets are received from aplurality of patients. The datasets comprise information about (1) aplurality of patient actions; (2) a plurality of disease symptoms; and(3) a plurality of patient characteristics. Next, at block 402, thepatient datasets are stored in a patient database. The patient databasemay be implemented in any type of data storage, including but notlimited to data storage 110 shown and described with reference to FIG.1.

In operation, the datasets from the plurality of patients are receivedover time. For example, certain patient characteristics may be receivedwhen a patient sets up an initial patient medical profile (e.g., sex,birthdate, height, weight, illnesses, allergies, etc.). The system mayreceive further patient characteristics or keep a running history ofcertain patient characteristics to supplement/update the patient'smedical profile as those characteristics change over time (e.g., weight,blood pressure, blood sugar level, temperature, etc.).

Different patient characteristics may be stored for different diseasesand/or disease symptoms because different patient characteristics may bemore important for some diseases and/or disease symptoms than forothers. For example, blood sugar may be more important for a patientwith diabetes but less important for a patient with recurring sinusinfections. Additionally, some patient characteristics may be collectedwith more frequency than others. For example, a patient's blood pressuremight be collected daily, a patient's weight might be collected weekly,and a patient's age might be collected only once.

After receiving patient datasets at block 401 and storing the patientdatasets in the patient database at block 402, algorithm 400 proceeds toblocks 403 and 404, where algorithm 400 determines whether correlationsexist between (1) sets of one or more particular patient actions and (2)any particular disease symptoms stored in the patient database. Thedetailed historical data collected from a plurality of patients enablesthe algorithm to look for correlations between disease symptoms and setsof one or more individual actions. For example, if the disease ismigraine headaches, and the symptom is nausea, the system can determinewhether there is a correlation between (1) migraine-induced nausea and(2) consuming more than 200 mg of caffeine in a day. Similarly, thesystem can determine whether there is a correlation between, forexample, (1) migraine-induced nausea and (2) a combination of (a)getting an average of 8 hours of sleep each night, (b) performing 20minutes or more of aerobic exercise at least three times a week, and (c)consuming more than 200 milligrams of caffeine in a day. Such acorrelation can be determined by using one or more different statisticalmethods, including but not limited to the statistical methods describedherein.

If at block 403, the algorithm 400 determines that the data in thepatient database does not show a correlation between a particularpatient action j and a particular disease symptom k, algorithm 400proceeds to block 404 where a new action/symptom set (j,k) is selected.Although block 404 shows an action/symptom set (j,k) as having a singleaction and a single symptom, some embodiments may use an action/symptomset having more than one action as described above (e.g., anaction/symptom set (j₁,j₂,k) or (j₁,j₂,j₃, k)).

After selecting a new action/symptom set, the algorithm 400 returns toblock 403 to determine whether a correlation exists between the patientaction and disease symptom of the new action/symptom set. Becausedatasets are collected from patients over time, new data may indicate acorrelation between a particular action and a particular symptom onlyafter a statistically significant set of data is available for analysis.Therefore, even though the statistical analysis performed at block 403may not initially indicate a correlation between the action and symptomof a particular action/symptom set, the algorithm 400 is configured toperiodically reconsider whether a correlation exists either on a regularschedule or perhaps in response to a query as described elsewhereherein.

If the algorithm 400 determines at block 403 that the data in thepatient database shows a correlation between a particular patient actionj and a particular disease symptom k, the algorithm 400 proceeds toblock 405 where the determined correlation is stored in a correlationdatabase. The correlation database may be implemented in any type ofdata storage, including but not limited to data storage 110 shown anddescribed with reference to FIG. 1.

After storing the determined correlation in the correlation database atblock 405, the algorithm 400 proceeds to block 406, where the algorithmbegins the process of verifying whether and the extent to which thedetermined correlation (action j, symptom k) exists in different patientgroups.

In preferred embodiments, patients are organized into test groups basedon at least one shared patient characteristic, i. For example, a patientcharacteristic may correspond to the sex, age (or age range), height,weight, ratio of height to weight, ethnicity, allergy, frequency ofdisease symptom, manifestation of disease symptom, etc. In someembodiments, patients may be organized into test groups based on amultiple shared patient characteristics. For example, a test group mayinclude men of a particular ethnicity within a certain age range andhaving a particular type of disease symptom manifestation. However, evenin such embodiments, all of the patients in the test group will share atleast one common patient characteristic.

In operation, the patients for the test group may be selected in manydifferent ways. For example, in some embodiments, the patients for aparticular test group may be selected and grouped by the system based oninformation in the patient database. In other embodiments, the systemmay solicit patients having certain patient characteristics to join atest group. In still further embodiments, the system may respond tosolicitations from patients to join a particular test group. In yetstill further embodiments, patients may be selected for a particulartest group based on any combination of the aforementioned methodologies.

In the example shown in FIG. 4, the algorithm 400 selects patients forpatient group i, where each patient in patient group i has at leastpatient characteristic i in common After selecting a set of patients fortest group i at block 407, the algorithm 400 proceeds to block 408,where patients in test group i are instructed to perform action j of thedetermined correlation (action j, symptom k). Preferably, some of thepatients in test group i are instructed to refrain from performingaction j for some defined period of time (e.g., two weeks, four weeks,etc.) to baseline the patients in test group i.

Then, at block 409, responses from the patients in test group i arecollected. The responses preferably include information on whether andthe extent to which performing action j had an effect on each patient'sdisease symptom k.

After collecting responses from the patients in test group i at block409, algorithm 400 then proceeds to block 410 where the correlation(positive or negative) between action j, symptom k, and patientcharacteristic i is stored in the correlation database. A positivecorrelation means that action j had a positive effect (improved) ondisease symptom k for patients having characteristic i. Likewise, anegative correlation means that action j had a negative effect(worsened) on disease symptom k for patients having characteristic i. Bystoring the correlation (positive or negative) between action j, symptomk, and characteristic i, the system is better able to predict, for a newpatient having characteristic i, whether performing action j willimprove or worsen disease symptom k.

In some embodiments, the algorithm may use the response data to assign aconfidence factor to the positive or negative correlation between actionj, symptom k, and characteristic i. In such embodiments, if a particularcorrelation has a sufficiently high confidence factor (e.g., aconfidence factor that exceeds a predetermined confidence factorthreshold), the system may test the correlation on other test groupscomprising patients having at least patient characteristic i. In thismanner, the system can test the importance of patient characteristic ito the correlation between action j and symptom k.

Some embodiments may additionally include blocks 412-415 for furtherimproving the confidence factor associated with the correlation betweenpatient characteristic i, action j, and symptom k. At step 412, a testgroup i′ is selected from the plurality of patients. Each patient intest group i′ lacks characteristic i. The patients for test group i′ maybe selected by any of the selection methodologies described for testgroup i (i.e., selected based on the information in the patientdatabase, soliciting patients lacking characteristic i, and/or inresponse to solicitations from patients to join test group i′.)

After selecting patients for test group i′ at block 412, algorithm 400proceeds to block 413, where patients in test group i′ are instructed toperform action j of the determined correlation (action j, symptom k).Preferably, some of the patients in test group i′ are instructed torefrain from performing action j for some defined period of time (e.g.,two weeks, four weeks, etc.) to baseline the patients in test group i′.

Then, at block 414, responses from the patients in test group i′ arecollected. The responses preferably include information on whether andthe extent to which performing action j had an effect on each patient'sdisease symptom k.

After collecting responses from the patients in test group i′ at block414, algorithm 400 then proceeds to block 415 where the correlation(positive or negative) between action j, symptom k, and patientcharacteristic i′ is stored in the correlation database. A positivecorrelation means that action j had a positive effect (improved) ondisease symptom k for patients lacking characteristic i. Likewise, anegative correlation means that action j had a negative effect(worsened) on disease symptom k for patients lacking characteristic i.By storing the correlation (positive or negative) between action j,symptom k, and the lack of characteristic i, the system is better ableto predict the likelihood that characteristic i is a factor in whetherperforming action j improves or worsens symptom k.

By determining and testing correlations in selected patient test groupsaccording to the method of FIG. 4, the system encourages patients tosupply action and symptom data (j, k) that will be most useful inconfirming or disproving determined correlations between particularactions and disease symptoms across many different patient groups havingvaried patient characteristics.

In some embodiments, the disclosed methods may be implemented ascomputer program instructions encoded on a non-transitorycomputer-readable storage media in a machine-readable format, or onother non-transitory media or articles of manufacture. FIG. 5 shows aschematic illustrating a conceptual partial view of an example computerprogram product that includes a computer program for executing acomputer process on a computing device, arranged according to at leastsome embodiments presented herein.

In one embodiment, the example computer program product 500 is providedusing a signal bearing medium 501. The signal bearing medium 501 mayinclude one or more programming instructions 502 that, when executed byone or more processors may provide functionality or portions of thefunctionality described above with respect to FIGS. 1-4. In someexamples, the signal bearing medium 501 may encompass acomputer-readable medium 503, such as, but not limited to, a hard diskdrive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape,or other forms of computer-based memory, etc. In some implementations,the signal bearing medium 501 may encompass a computer recordable medium504, such as, but not limited to, memory, read/write (R/W) CDs, R/WDVDs, etc. In some implementations, the signal bearing medium 501 mayencompass a communications medium 505, such as, but not limited to, adigital and/or an analog communication medium (e.g., a fiber opticcable, a waveguide, a wired communications link, a wirelesscommunication link, etc.). Thus, for example, the signal bearing medium501 may be conveyed by a wireless form of the communications medium 505(e.g., a wireless communications medium conforming with the IEEE 802.11standard or other transmission protocol).

The one or more programming instructions 502 may be, for example,computer executable and/or logic implemented instructions. In someexamples, a computing device such as the web server 102 or a clientdevice 108 a-c of FIG. 1 may be configured to provide variousoperations, functions, or actions in response to the programminginstructions conveyed to the computing device by one or more of computerreadable medium 503, the computer recordable medium 504, and/or thecommunications medium 505.

While particular aspects and embodiments are disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art inlight of the foregoing teaching. The various aspects and embodimentsdisclosed herein are for illustration purposes only and are not intendedto be limiting, with the true scope and spirit being indicated by thefollowing claims.

What is claimed is:
 1. A method comprising: receiving, at a computingdevice via a graphical user interface, a dataset from a user, whereinthe dataset comprises information about at least one disease symptom ofthe user and at least one particular action of the user; storing thedataset in a database comprising a tangible, non-transitory computerreadable media; receiving a plurality of datasets from a correspondingplurality of users, each user having at least one corresponding diseasesymptom, wherein an individual dataset for an individual user includesinformation about at least one disease symptom of the individual userand at least one action of the individual user; storing the receivedplurality of datasets in the database; determining a threshold amount ofdata for determining a predicted effect of the particular action on oneor more disease symptoms for the user; in response to reaching thethreshold amount of data for determining the predicted effect of theparticular action, displaying at the graphical user interface anindication to the user, wherein the indication comprises arecommendation to the user regarding the particular action based on thedetermined predicted effect between the particular action and the one ormore disease symptoms of the user; in response to not reaching thethreshold amount of data for determining the predicted effect of theparticular action, displaying at the graphical user interface a prompt,wherein the prompt comprises an instruction to the user to enter moredata over time; based on the user receiving the prompt, collecting aresponse from the user via the graphical user interface, wherein theresponse comprises information about at least one disease symptom of theuser and at least one action of the user; determining, by a machinelearning system, a correlation between one or more actions and one ormore disease symptoms, wherein determining the correlation comprisesusing a machine learning technique to model the actions and diseasesymptoms in the plurality of datasets received from the correspondingplurality of users; storing in the database the correlation between theone or more actions and the one or more disease symptoms determined fromthe plurality of datasets; and providing a recommendation to the userbased on the determined correlation between the one or more actions andthe one or more disease symptoms.
 2. The method of claim 1, wherein theat least one action includes at least one of (i) consuming or refrainingfrom consuming a food product, (ii) ingesting or refraining fromingesting a therapeutic agent, (iii) applying or refraining fromapplying a therapeutic agent, (iv) ingesting or refraining fromingesting a dietary supplement, (iv) performing or refraining fromperforming a physical activity, (v) exposure to or avoiding exposure toa chemical agent, or (vi) exposure to or avoiding exposure to anenvironmental factor.
 3. The method of claim 2, wherein the at least oneaction further includes a quantification of at least one aspect of theat least one action.
 4. The method of claim 1, wherein the databaseincludes a medical profile for the user comprising one or more of anage, a sex, a height, a weight, and a medical history of the user. 5.The method of claim 1, further comprising: receiving a query regardingwhether a correlation exists between a particular action and aparticular disease symptom; and in response to receiving the query,querying the database to determine whether the database includes thecorrelation between the particular action and the particular diseasesymptom; and sending an indication of whether the database includes thecorrelation between the particular action and the particular diseasesymptom.
 6. The method of claim 1, further comprising: sendinginstructions to the user to perform a specific action, wherein thespecific action is selected to test a particular correlation stored inthe database; receiving, from the user, inputs associated with theperformance of the specific action of the user and at least one diseasesymptom of the user; updating the particular correlation based on theinputs received from the user; and storing the particular correlation inthe database.
 7. A system comprising: one or more processors configuredto: (1) receive, via a graphical user interface, a dataset from a user,wherein the dataset includes information about at least one diseasesymptom of the user and at least one particular action of the user; (2)store the dataset in a database comprising a tangible, non-transitorycomputer readable media; (3) receive a plurality of datasets from acorresponding plurality of users, each user having at least onecorresponding disease, wherein an individual dataset for an individualuser includes information about at least one disease symptom of theindividual user and at least one action of the individual user; (4)store the received datasets in the database; (5) determine a thresholdamount of data for determining a predicted effect of the particularaction on one or more disease symptoms for the user; (6) in response toreaching the threshold amount of data to determine the predicted effectof the particular action, display at the graphical user interface anindication to the user, wherein the indication comprises arecommendation to the user regarding the particular action based on thedetermined predicted effect between the particular action and the one ormore disease symptoms of the user; (7) in response to not reaching thethreshold amount of data for determining the predicted effect of theparticular action, display at the graphical user interface a prompt,wherein the prompt comprises an instruction to the user to enter moredata over time; (8) based on the user receiving the prompt, collect aresponse from the user via the graphical user interface, wherein theresponse comprises information about at least one disease symptom of theuser and at least one action of the user; (9) determine, by a machinelearning system, a correlation between one or more actions and one ormore disease symptoms, wherein determining the correlation comprisesusing a machine learning technique to model the actions and diseasesymptoms in the plurality of datasets received from the correspondingplurality of users; (10) store in the database the correlation betweenthe one or more actions and the one or more disease symptoms determinedfrom the plurality of datasets; and (11) provide a recommendation to theuser based on the determined correlation between the one or more actionsand the one or more disease symptoms.
 8. The system of claim 7, whereinthe one or more processors are further configured to determine acorrelation between one or more actions and one or more disease symptomsbased on a statistical analysis of the actions and disease symptoms inthe dataset by performing a multivariate analysis of the actions anddisease symptoms stored in the database.
 9. The system of claim 7,wherein the one or more processors are configured to determine acorrelation between one or more actions and one or more disease symptomsbased on a statistical analysis of the actions and disease symptoms inthe dataset by modeling a Boolean network of the actions and symptomsstored in the database.
 10. The system of claim 7, wherein the one ormore processors are further configured to: (1) receive a query regardingwhether a correlation exists between the particular action and aparticular disease symptom; (2) in response to receiving the query,query the database to determine whether the database includes thecorrelation between the particular action and the particular diseasesymptom; (3) in response to determining that the database includes thecorrelation between the particular action and the particular diseasesymptom, send an indication of the correlation; and (4) in response todetermining that the database does not include the correlation betweenthe particular action and the particular disease symptom, send anindication that the database does not include the correlation betweenthe particular action and the particular disease symptom.
 11. The systemof claim 7, wherein the one or more processors are further configuredto: (1) initiate transmission of instructions to a user to perform aspecific action, wherein the specific action is selected to test aparticular correlation stored in the database; (2) receive, from theuser, inputs associated with the performance of the specific action andat least one disease symptom of the user; (3) update the particularcorrelation based on the inputs received from the user; and (4) storethe updated correlation in the database.
 12. The system of claim 11,wherein the one or more processors are configured to update theparticular correlation based on the inputs received from the user by:(1) updating the database to include the inputs received from the user;and (2) performing at least one of (a) a multivariate analysis of theactions and disease symptoms stored in the updated database or (b) amultivariate analysis of the inputs associated with the performance ofthe specific action of the user and at least one disease symptom of theuser.
 13. The method of claim 1, wherein the recommendation comprises atleast one of (i) a recommendation to avoid taking the particular actionbased on the particular action being associated with one or moreworsened disease symptoms for the user, and (ii) a recommendation toundergo taking the particular action based on the particular actionbeing associated with one or more improved disease symptoms for theuser.
 14. The method of claim 1, wherein determining the correlationbetween one or more actions and one or more disease symptoms comprises:receiving the plurality of datasets from the corresponding plurality ofpatients, each patient having at least one corresponding disease incommon and one or more corresponding patient characteristic in common,wherein the individual dataset for an individual patient includesinformation about (i) one or more disease symptoms of the individualpatient, (ii) one or more patient actions performed by the individualpatient, and (iii) one or more characteristics of the individualpatient; storing the plurality of datasets in a patient database;displaying, via the graphical user interface, an instruction to thepatients to perform at least one action; receiving, via the graphicaluser interface, information comprising an extent to which performing theat least one action affects one or more disease symptoms; determining afirst correlation between the at least one action and the one or moredisease symptoms; and storing the first correlation in a correlationdatabase.